Malnutrition Matters in Canadian Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: Malnutrition is common in Canadian hospitalized patients, yet system-wide malnutrition screening is not mandatory in Canada. AIMS: Our goal was to define the point prevalence of malnutrition risk at a major tertiary care center in Hamilton, Ontario, using the Malnutrition Universal Screening Tool (MUST) to determine feasibility of hospital-wide screening in the Canadian context. METHODS: After research ethics approval was obtained, we arranged for a clinical nutrition support team to conduct the MUST screening on all inpatients at Hamilton Health Sciences, Juravinski site, a large academic acute care hospital. RESULTS: A total of 315 patients were included (female, n = 160 [51%]; male, n = 155 [49%]; average age, 71 years). We identified 31% at high risk for malnutrition and 14% at medium risk, keeping with reported rates of malnutrition in the literature. Survey of dietitians and interns indicated that the MUST was easy to use and perform and that they had support of their unit supervisors. All respondents thought that the screen was useful and they wanted to repeat it. CONCLUSION: The MUST is an easy and efficient way to define point prevalence of malnutrition risk in Canadian hospitalized patients. Moving to system-wide nutritional screening will bring about the best practices in nutrition care with the involvement of key stakeholders and decision makers. Nutritional screening will allow us to utilize nutrition resources more efficiently, engage administrators in addressing shortfalls in nutrition care, and form a baseline for which to measure the efficacy of future nutritional interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".